code: def center_to_corners_format(bboxes_center: TensorType) -> TensorType:
      """
      Converts bounding boxes from center format to corners format.
  
      center format: contains the coordinate for the center of the box and its width, height dimensions
          (center_x, center_y, width, height)
      corners format: contains the coodinates for the top-left and bottom-right corners of the box
          (top_left_x, top_left_y, bottom_right_x, bottom_right_y)
      """
      # Function is used during model forward pass, so we use the input framework if possible, without
      # converting to numpy
      if is_torch_tensor(bboxes_center):
          return _center_to_corners_format_torch(bboxes_center)
      elif isinstance(bboxes_center, np.ndarray):
          return _center_to_corners_format_numpy(bboxes_center)
      elif is_tf_tensor(bboxes_center):
          return _center_to_corners_format_tf(bboxes_center)
  
      raise ValueError(f"Unsupported input type {type(bboxes_center)}")
  
imports: import warnings
  from typing import Iterable, List, Optional, Tuple, Union
  import numpy
  from .image_utils import ChannelDimension, ImageInput, get_channel_dimension_axis, get_image_size, infer_channel_dimension_format
  from .utils import ExplicitEnum, TensorType, is_jax_tensor, is_tf_tensor, is_torch_tensor
  from .utils.import_utils import is_flax_available, is_tf_available, is_torch_available, is_vision_available, requires_backends
label_desc: Use image node_id 12 for calling a global function with example usage: center_to_corners_format(bboxes_center) and returns: _center_to_corners_format_torch, _center_to_corners_format_numpy, _center_to_corners_format_tf